In this short assay we shall discuss a couple of papers previously published. The first paper was published by Hajirasouliha and Elemento in the American Society for Reproductive Medicine. The authors summed up the concepts of precision medicine and Artificial Intelligence (AI), as well as the several limitations that these have in medicine when genomics data are used. The second paper was published by Dias and Torkamani in Genome Medicine. The authors summed up the recent successes and potential future applications of AI in clinical diagnostics, highlighting the problems that can appear, and how can be resolved using AI techniques.

Taking into account previous information published by Iman Hajirasouliha and Olivier Elemento in the American Society for Reproductive Medicine and by Raquel Dias and Ali Torkamani in Genome Medicine, the main message of both papers is that applying AI to medicine has lots of benefits, despite having some limitations. Comparing the second paper and the first paper, the second one (published by Raquel Dias and Ali Torkamani in Genome Medicine) is more focused on the capacity that has AI to solve the problems that can appear when genomics data is used. Although this paper explains the solutions to the major problems when AI is combined with genomics data, both of us share the same opinion about the need of applies AI due to the variety and complexity of this type of data. Finally, both papers have in common that more studies are needed.

Personally, I think that the two previous papers are complementary each to other. This is because the first one published by Iman Hajirasouliha and Olivier Elemento in the American Society for Reproductive Medicine explains the problems that may appear when genomics data and AI are used, whereas the second one published by Raquel Dias and Ali Torkamani in Genome Medicine suggest solutions using AI, so the authors of the first paper might take advantage of the second one when more studies related to this subject are done.

To sum up, in my opinion applying AI in medicine is a potential tool that in a near future should be implemented in almost public and private institutes. I agree that at the moment there exist some limitations, so in my view, more studies must be done.

In this short assay we shall discuss a couple of papers previously published. The first paper was published Titus Slavici and Bogdan Almajan in the Journal of Rehabilitation Medicine. The first review was published by Titus Slavici and Bogdan Almajan in the Journal of Rehabilitation Medicine. The objective of this study is to construct an application with which it will be possible to determine the most effective type of physiotherapy exercise for improving the health state of an individual with Cystic Fibrosiss (CF). The second paper was published by Eva J. Zuckera, Zachary A. Barnesb, et al, in the Journal of Cystic Fibrosis. This study aimed to evaluate the hypothesis that a DCNN model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with CF, performing similarly to a pediatric radiologist.

Taking into account what the previous papers explained, the authors have in common that they have developed an AI model to apply to CF in order to improve the health of the patients that suffer from this disease. The authors of the first paper have developed an ANN model to predict the most suitable physiotherapy exercise for each patient, whereas the authors of the second paper have developed a DCNN model that can predict Brasfield scoring of CXRs for patients. Although the models used are different, both of them are focused on AI.

I personally think that the two papers are complementary to each other despite the aim of each study is not the same, because if you can first monitor the respiratory disease in the individual, you have more information about it and consequently you will predict a more appropriate physiotherapy exercise.

In conclusion, in my opinion, AI is a very useful instrument that may help in the quality of medical services. Also, considering that fewer experimental processes are needed, the amount of money utilized will be lower.

Nanotechnology is gaining significant attention worldwide for cancer treatment. Nanoparticles are being used as Nanomedicine which participates in diagnosis and treatment of various diseases including cancer. Nanobiotechnology encourages the combination of diagnostics with therapeutics, which is a vital component of a customized way to deal with the malignancy. In this review, the use of nanotechnology on the treatment and diagnosis of cancer will be discussed.

During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. Different applications of Machine Learning on COVID-19 will be discussed.

Cocaine is the second most used illicit drug after cannabis in the general population. It is a powerful central nervous system stimulating agent and a highly addictive drug. In this review two different research made in cocaine-vaccine will be discussed.

Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is one of the major burdens of disease in developing countries, and the standard-of-care treatment includes prescribing antiretroviral drugs. Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can lead to drug resistance. Predicting drug resistance before treatment is crucial for individual treatments. Taking that into account, different investigations undertaken with machine learning will be discussed.

Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. In this review medical imaging for different applications will be discussed. In addition, different startup, spin-off, Small and Medium Enterprises (SMEs), and also some BigPharman and or Tech companies are developing AI-based medical imaging technologies. This communication also lists some of these startup companies.

Antihistamines are responsible for blocking histamine receptors, thus reducing the effects of this amine on the body. However, the experimental classification of these compounds is accompanied by several limitations such as the high time invested and the consumption of large amounts of resources. QSAR methods reduce the cost and time spent discovering new drugs. The objective of the present study was to model the antihistamine activity of a series of compounds reported in the literature for the identification of new therapeutic candidates. For this, the calculation of the spectral moments of the adjacency matrix between edges of the molecular graph with different parameters that characterize the molecules of 90 active and 250 inactive compounds was carried out. using the MODESLAB methodology. 91 descriptors related to the activity of these drugs were calculated, which were used in a training series divided into two groups. In order to identify the descriptors that best discriminate and define their set of functions, a linear discriminant analysis was developed using the step-bystep inclusion method using the IBM SPSS version 22 statistical software. A function was generated that constitutes linear combinations of eight molecular descriptors, which encode both steric and electronic information of the molecules of each group. The functions obtained present a Wilks Lambda of (0.421) and a high canonical correlation of (0.8351), which shows its discriminating power, and it makes it possible to use the descriptors included in them in future studies of the structure-property or structure-activity relationship. The results obtained suggest the use of this model with a high predictive value (prediction coefficient of 82.19%) in the determination of compounds with antihistamine activity.

The degree of lipophilicity of a drug, defined by its partition coefficient, is a key parameter to understand and analyze the activity of said compound in the body. In the case of drugs active on the central nervous system, the value of the partition coefficient or log P indicates their ability to cross the blood-brain barrier and carry out their pharmacological action. However, the experimental determination of this property is accompanied by several drawbacks, since the methods used for this purpose are usually time-consuming, resource-intensive, and expensive equipment, in addition to being prone to measurement errors. To solve these problems, computational molecular modeling methods are used. Among them are the QSPR studies, with which quantitative relationships are established between the structural characteristics of the analyzed compounds and the property of interest. In the present work, a predictive model was built based on the partition coefficient as a property of interest. For this, a training series of 286 active drugs on the central nervous system was built, obtaining their structures, simplified representations and experimental values of the partition coefficient using the ACDLabs software. The MODESLAB program was used to calculate a total of 91 molecular descriptors and based on these, the BuildQSAR software was used to obtain the predictive model

Global energy consumption and concern with environmental contamination have led to an increased interest on the harvesting of solar energy to conduct chemical reactions that can provide cleaner sources of energy and raw materials or even neutralize hazardous chemicals. The combination of a zirconium metal-organic cluster and a Keggin type polyoxotungstate into a compound of formula [Zr₆(µ₃-O)₄(µ₃-OH)₄(µ-OOCC₆H₅)₈(H₂O)₈][SiW₁₂O₄₀] led to a chemically and photochemically stable material in which a synergistic effect between the metal-organic cluster and the polyoxometalate allows to markedly overpass the permanent porosity of the referential compounds, the hybrid material surpasses their methylene blue adsorption capacity (Zr6/W12-NP: 73 µg/mg; Cs/W12: 63 µg/mg; UiO-66: 16 µg/mg). Interestingly, the photochemical activity was also compromised to a the quantitative photodegradation of the dye in 4 h (k = 7.7·10^–3 min^–1), while the referential compounds did not exhibit any substantial activity toward the photooxidation of methylene blue.